What is Lakehouse for Apache Iceberg?
Lakehouse for Apache Iceberg, known as BigLake until April 2026, is Google’s storage engine for open data lakehouses. The service combines the open Apache Iceberg table format with fully managed, enterprise-grade storage infrastructure on Google Cloud, providing a unified interface for analytics and AI workloads.
At its core is the Lakehouse Runtime Catalog (formerly BigLake metastore), a serverless metadata service that serves as a single source of truth for table metadata across different query engines, removing the need to synchronize metadata between systems. Existing BigLake APIs, client libraries, CLI commands, and IAM names remain unchanged.
Core Features
- Open Iceberg Format: Native support for Apache Iceberg V2 (GA) and V3 (preview) tables
- Lakehouse Runtime Catalog: Central, serverless metadata catalog for consistent governance
- Multi-Engine Access: Query the same data with BigQuery, Apache Spark, Flink, Hive, or Trino
- Fine-grained Security: Row-level security and column masking on lakehouse data
- Cross-Cloud Lakehouse (Preview): Access Iceberg data in AWS S3 or Azure without moving data
Typical Use Cases
Open Data Lakehouse Architecture
Combination of data lake flexibility with data warehouse capabilities. Iceberg tables enable ACID transactions, time travel, and schema and partition evolution on Parquet data.
Unified Data Governance
One governance framework for all Iceberg data in the organization. IAM policies are consistently enforced through the Lakehouse Runtime Catalog, regardless of which engine accesses the data.
Multi-Cloud Analytics
With Cross-Cloud Lakehouse, analytics and AI workloads can access Iceberg data residing in other clouds without ETL processes or additional copies.
Benefits
- No data movement required for analytics
- Avoid vendor lock-in through the open Iceberg format
- Consistent security across all query engines
- Cost-effective through separation of storage and compute
Integration with innFactory
As a certified Google Cloud partner, innFactory supports you with Lakehouse for Apache Iceberg: lakehouse architecture, migration from Hadoop/Hive or existing BigLake environments, Iceberg table design, and governance implementation. We help modernize your data platform.
Available Tiers & Options
Lakehouse for Apache Iceberg
- Unified governance
- Open Iceberg table format
- Multi-engine access
- Apache Iceberg only as table format
Typical Use Cases
Frequently Asked Questions
What is Lakehouse for Apache Iceberg?
Lakehouse for Apache Iceberg (formerly BigLake) is Google's storage engine for open data lakehouses. It provides unified access to Iceberg tables in Cloud Storage across engines such as BigQuery, Spark, Flink, Hive, or Trino.
Which table formats are supported?
The service supports Apache Iceberg V2 tables (generally available) and V3 tables (preview). Earlier support for Delta Lake and Apache Hudi is no longer the focus of the current product, which is centered on Apache Iceberg as the open standard.
What was BigLake and what is the Lakehouse Runtime Catalog?
BigLake was renamed to Lakehouse for Apache Iceberg in April 2026. The former BigLake metastore is now called the Lakehouse Runtime Catalog, a fully managed, serverless metadata service acting as a single source of truth across systems. APIs, client libraries, and IAM names still reference BigLake.
How does multi-cloud access work?
With Cross-Cloud Lakehouse (preview), metadata from other cloud providers such as AWS S3 or Azure can be synchronized without moving the underlying data. This allows analytics and AI workloads to run across providers on Iceberg data.
Note: All product information on this page has been compiled with care, but is provided without guarantee and may be outdated or incomplete. Cloud services evolve rapidly — features, pricing, SLAs, and availability change frequently. Authoritative and up-to-date information can only be found on the official product page of Google Cloud (official documentation). This page does not represent an offer by Google Cloud.
